2018年3月1日
Efficient reformulation of 1-norm ranking SVM
IEICE Transactions on Information and Systems
- ,
- ,
- 巻
- E101D
- 号
- 3
- 開始ページ
- 719
- 終了ページ
- 729
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1587/transinf.2017EDP7233
- 出版者・発行元
- Institute of Electronics, Information and Communication, Engineers, IEICE
Finding linear functions that maximize AUC scores is important in ranking research. A typical approach to the ranking problem is to reduce it to a binary classification problem over a new instance space, consisting of all pairs of positive and negative instances. Specifically, this approach is formulated as hard or soft margin optimization problems over pn pairs of p positive and n negative instances. Solving the optimization problems directly is impractical since we have to deal with a sample of size pn, which is quadratically larger than the original sample size p + n. In this paper, we reformulate the ranking problem as variants of hard and soft margin optimization problems over p+n instances. The resulting classifiers of our methods are guaranteed to have a certain amount of AUC scores.
- リンク情報
- ID情報
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- DOI : 10.1587/transinf.2017EDP7233
- ISSN : 1745-1361
- ISSN : 0916-8532
- DBLP ID : journals/ieicet/SuehiroHT18
- SCOPUS ID : 85042679921